For a Dot Product decoder, the problem of finding the top scoring neighbors can be approximated efficiently.
This is a well-studied problem, known as approximate maximum inner product search (MIPS) [1]}, [2]} (see Sec. REF  for a comprehensive literature review).
MIPS techniques allow Dot Product' inference to be completed in a few milliseconds, even with millions of neighbors.
There exists some work that tries to extend MIPS to the ConcatMLP [3]}, [4]}.
These methods hold strict assumptions on the models' training and are not directly applicable to the HadamardMLP.
To the best of our knowledge, no such sublinear techniques exist for the top scoring neighbor retrieval with the HadamardMLP [5]}, which is a complex nonlinear function.
